Multi-fidelity and learning-regularization for single image super resolution

Abstract

Ill-posedness results in regularization-based methods being widely used in single image super-resolution (SISR). However, producing super-resolved images with finer details and fewer artifacts is still a great challenge. With the help of the plug-and-play framework, we introduce a novel fidelity term and learned prior knowledge to produce a powerful SISR model. In the proposed fidelity term, called multi-fidelity, the similarity between the observed data and simulated data is measured in terms of both intensity and gradient; these measurements can reflect the tendency of feature response results to degrade into multiple image layers. Based on the half quadratic splitting (HQS) method, the proposed SISR model is split into two sub-problems, which include the multi-fidelity and regularization terms, respectively. In this paper, we design a deep network, named as R&BED, to learn prior image knowledge. Compared with the manually designed regularization term, learned knowledge can preserve easily ignored features. Experimental results indicate that the subjective and objective metrics corresponding to the proposed method are better than those obtained using the comparison methods.